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Big Data Opportunities in Green Supply Chain Management

Introduction & Problem Motivation

According to academic research in the field of supply chain management (SCM), the industry is quickly changing because of the advent of digital instruments that are utilized to speed up supply chain processes and reduce the involvement of human workers. According to Hartley and Sawaya (2019), artificial intelligence (AI) is one of the most vivid establishments in the area because it encompasses a large number of initiatives that can be utilized to transform the whole notion of SCM completely. The Fourth Industrial Revolution mildly forces businesses to lean toward digital solutions and dehumanize their SCMs in order to prevent human error and ride the wave of technological advancements masterfully (Alzoubi, 2018). As the lines between different spheres of human lives are blurring, different biological, physical, and digital factors might have to be reconsidered in the face of an AI-based revolution where lots of data will be processed by machines.

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Problem Statement

The problem that the author reviewed within the framework of the current research project was the growing pool of green SCM knowledge that got interconnected with technology, such as Big Data. Nevertheless, there are also practical and theoretical implications that become evident after looking at the subject of green SCM from several unique perspectives. The problem that the author investigated within this research project was the presence of solutions based on Big Data that were implemented to improve green SCM operations and benefits employees at the same time.

Background & Literature Review

The growing level of interconnectedness between devices that are included in supply chains shows that humans have to investigate new ways of transmitting and analyzing information which might also be utilized to perform predictions and automate some of the operations. The generation of data leads to a situation where there is an increased need to meet the growing demand for digitalization and respond to it by investing in AI, Machine Learning, Big Data, or any other technology that is of interest to the respective organization (Min et al., 2019). This context makes it safe to say that machine-generated content could be much more carefully curated through the prism of a framework that includes “green” initiatives.

The importance of Big Data for green SCM cannot be either underestimated or ignored because accurate decision-making, even if computerized, depends on the amount of evidence available to the actor reaching the verdict (Baryannis et al., 2019). The future SCM trends are going to be associated with an even broader extent of computerization. It should be rational to assume that a simultaneous adoption of several automated tools for green SCM could become both a blessing and a curse for the organization (Alzoubi, 2018; Chehbi-Gamoura et al., 2020). Supply chain performance, therefore, has to be observed from several viewpoints in order not to focus on just one technology, such as the Internet of Things, for example (Hartley and Sawaya, 2019; Min et al., 2019). Practical and theoretical perspectives on the digitalization of supply chains suggest that possible drawbacks should be addressed first.

Data

The capabilities of green SCM were viewed through the prism of a systematic literature review on the subject of the use of Big Data and the collection of survey responses from supply chain employees. The application of a systematic literature review also responds to the purpose of the current research because it gave the researcher the opportunity to (a) summarize evidence without compressing it or leaving out certain details, (b) explore the question from several perspectives, and (c) compile a reliable knowledge base from the existing evidence and newly collected survey responses. All the relevant information collected from articles was analyzed in accordance with the Critical Appraisal Skills Program (CASP) worksheet so as to pick studies of the highest quality. The data was analyzed with the help of the SPSS software package in order to identify the key trends in both survey respondents’ answers and relevant literature on the subject. The parallels between data from the literature and the survey results were necessary to pinpoint the essential inclinations across the industry that should be either fortified or abandoned.

Model and Analysis

A total of 15 articles were picked for the systematic review of literature in order to provide the researcher with additional information on key methods of Big Data analytics and how those could add to green SCM. The author focused on the importance of data mining, machine learning, and statistics to highlight the value of Big Data and reinforce the idea that multiple inferences and conclusions might be made from a detailed review of the literature. The key three areas of green SCM were picked to generate deeper insight into the potential advantages of Big Data in SCM and its future applications: internal management practices, green purchasing, customer support for green initiatives, and general green SCM (Hartley and Sawaya, 2019; Min et al., 2019). These topics were also aligned against survey responses in order for the researcher to compile a data set that would respond to the question of whether Big Data contributes to SCM in a positive manner.

From the existing evidence, it was found that internal management practices were crucial because they could pave the way for improvements related to the Intra organizational environment (Tseng et al., 2019). Green purchasing could be defined as the second most important topic because it could be associated with policies related to procurement and raw materials usage (Doolun et al., 2018). Customer support for green initiatives was linked to Zhao et al.’s (2017) idea that environmental performance could be improved under the strict guidance of individuals that were interested in direct participation and not just providing feedback.

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Results and Recommendations

Internal Management Practices

Based on the evidence obtained within the framework of the current paper, it may be stated that internal management practices depend on Big Data nowadays because of numerous opportunities for green innovation and eco-friendly design of supply chains (Gupta et al., 2019). This means that clean production is much easier to achieve as well, paving the way for more instances of correlational and causative analyses of what could be the essential contributors to efficient SCM (Doolun et al., 2018; Zhao et al., 2017). The potential of green SCM powered by Big Data may be deemed limitless because regular operation patterns would be extended with the help of huge data sets containing all kinds of information regarding customers, materials, logistics, and many more (Liu & Yi, 2017). Many respondents are looking forward to more instances of all-inclusive implementation of Big Data in green SCM because of powerful environmental management (Abdel-Baset et al., 2019; Singh & El-Kassar, 2019). One of the potential avenues of future research was the exploration of energy consumption patterns, as many respondents worried about the probability of saving that resource.

The key recommendation that stems from these findings is that manufacturing practices could become significantly more sustainable under the influence of Big Data and its derivatives, such as data mining, for example. The rationale behind this is that many organizations that choose to innovate digitally expect their decision-making actions to improve and enhance the degree of visibility within their supply chains. On the other hand, internal management practices based on green SCM could become one of the few means of strengthening contemporary competitive advantages through innovation and research. Green product design and advocacy for environment-friendly operations are required for organizations to cope with environmental challenges. These may include practically anything from interior temperature to air quality information that could be automatically transferred between different nodes of the existing supply chain.

Green Purchasing

Another essential concept that has to be considered by businesses expecting to benefit from green SCM is green purchasing. It means that the organization would constantly seek green suppliers and only partner with third-party organizations that are environment-friendly (Rajabion et al., 2019). In this case, Big Data would be rather helpful because it might provide manufacturers with insights into historical information and the potential trends in procurement (Choi, 2018). Digital analytics would provide the basis for a full-fledged examination of existing resources and their potential value for the organization. One of the examples from the literature is the possibility to utilize machine learning and optimization to collect data on suppliers who follow low carbon emission regulations (Ilyas et al., 2020; Song et al., 2019). According to survey respondents, on the other hand, Big Data could serve as a support system for green purchasing through the interface of cloud computing and real-time data updates. Therefore, supplier selection criteria represent a dynamic variable that would have to be monitored closely with the help of green SCM and massive data sets.

In terms of green purchasing, the recommendation would be to utilize Big Data to achieve a tradeoff between the quality of the final product and the possible carbon footprint. High-quality raw materials from green suppliers would be turned into environment-friendly products intended to appeal to a larger target customer base. Optimization would be required to make prices affordable and help more consumers gain access to green products. Nevertheless, in order to accomplish this, organizations would have to engage in collaboration activities first to be able to motivate suppliers to partner with them. This hypothesis also reflects the idea that sustainable performance might be required to meet environmental criteria and attract more vendors to corporate operations. Big Data analytics would bypass the synchronization step and allow the administration to share required insights with green suppliers right away.

Customer Support for Green Initiatives

The third crucial element of discussion is the presence of customer support for green initiatives established with the help of Big Data. Any organization that expects to achieve positive results, it may revolve around reverse logistics, constant two-way feedback, and smart transportation (Rahman et al., 2020). The existing capabilities of Big Data analytics allow for proper customer involvement in the processes of purchasing, production, and recycling (El-Kassar & Singh, 2019; Tiwari et al., 2018). Supply chain employees also pointed out the need to study eco-design trends and assess their viability for the organization. Green logistics is key to developing fruitful cooperation between companies and customers that expect to reduce the carbon footprint while maintaining the high quality of products delivered to consumers (Gawankar et al., 2020; Tseng et al., 2019). At the end of the day, customer support might assist organizations in terms of reducing resource consumption and making sure that environmental pollution is either absent or minimal.

The core recommendation for organizations planning to exploit Big Data analytics and develop green SCM would be to focus on the deployment of an optimization model that would connect consumers and employees via two-way feedback. The standard of environmental sustainability will be required to pave the way for smarter logistics activities that can be launched automatically and reduce the occurrence of human error. Also, more optimization algorithms might be required to ensure the proper incorporation of customer feedback into the system. Complex decision-making and green logistics are intertwined in a number of ways that require consumers to participate in the development of green supply chains.

Conclusions

The current research project shows that the accuracy of decision-making could be significantly improved with the help of Big Data and its proper connection to the given green supply chain. One of the possible ways of exploiting that knowledge would be to predict future trends using modern technology and investigate all the possible patterns in order to reduce business uncertainty and environmental footprint at the same time. The essential implication of the existing research project is that there is a need for green SCM initiatives that would be powered by Big Data and its derivatives. The descriptions obtained from employee surveys showed that many individuals are looking forward to a better understanding of how technology could be included in SCM agendas without putting a strain on the company’s budget and human resources. Another crucial implication of the findings presented above is that investment decision-making represents a struggle for organizations that do not have enough experience in green SCM.

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While taking into consideration all of the information above, the researcher also believes that future studies should pay more attention to how Big Data could influence the interconnectedness between different operations within the given supply chain. This means that none of the three factors discussed in this paper can be taken separately. A holistic framework might be necessary to answer all of the new questions that appear because of the large-scale implementation of Big Data analytics. The quick progress that organizations make in terms of deploying instruments based on Big Data is the most evident reason to acknowledge the future value of green SCM and its association with emerging innovations.

References

Abdel-Baset, M., Chang, V., & Gamal, A. (2019). Evaluation of the green supply chain management practices: A novel neutrosophic approach. Computers in Industry, 108, 210-220.

Alzoubi, H. (2018). The role of intelligent information systems in e-supply chain management performance. International Journal of Multidisciplinary Thought, 7(2), 363-370.

Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993-1004.

Chehbi-Gamoura, S., Derrouiche, R., Damand, D., & Barth, M. (2020). Insights from Big Data Analytics in supply chain management: An all-inclusive literature review using the SCOR model. Production Planning & Control, 31(5), 355-382.

Choi, T. M. (2018). A system of systems approach for global supply chain management in the big data era. IEEE Engineering Management Review, 46(1), 91-97.

Doolun, I. S., Ponnambalam, S. G., Subramanian, N., & Kanagaraj, G. (2018). Data-driven hybrid evolutionary analytical approach for multi-objective location-allocation decisions: Automotive green supply chain empirical evidence. Computers & Operations Research, 98, 265-283.

El-Kassar, A. N., & Singh, S. K. (2019). Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices. Technological Forecasting and Social Change, 144, 483-498.

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Gawankar, S. A., Gunasekaran, A., & Kamble, S. (2020). A study on investments in the big data-driven supply chain, performance measures and organizational performance in Indian retail 4.0 context. International Journal of Production Research, 58(5), 1574-1593.

Gupta, S., Altay, N., & Luo, Z. (2019). Big data in humanitarian supply chain management: A review and further research directions. Annals of Operations Research, 283(1), 1153-1173.

Hartley, J. L., & Sawaya, W. J. (2019). Tortoise, not the hare: Digital transformation of supply chain business processes. Business Horizons, 62(6), 707-715.

Ilyas, S., Hu, Z., & Wiwattanakornwong, K. (2020). Unleashing the role of top management and government support in green supply chain management and sustainable development goals. Environmental Science and Pollution Research, 27(8), 8210-8223.

Liu, P., & Yi, S. P. (2017). Pricing policies of green supply chain considering targeted advertising and product green degree in the big data environment. Journal of Cleaner Production, 164, 1614-1622.

Min, S., Zacharia, Z. G., & Smith, C. D. (2019). Defining supply chain management: In the past, present, and future. Journal of Business Logistics, 40(1), 44-55.

Rahman, T., Ali, S. M., Moktadir, M. A., & Kusi-Sarpong, S. (2020). Evaluating barriers to implementing green supply chain management: An example from an emerging economy. Production Planning & Control, 31(8), 673-698.

Rajabion, L., Khorraminia, M., Andjomshoaa, A., Ghafouri-Azar, M., & Molavi, H. (2019). A new model for assessing the impact of the urban intelligent transportation system, farmers’ knowledge and business processes on the success of the green supply chain management system for urban distribution of agricultural products. Journal of Retailing and Consumer Services, 50, 154-162.

Singh, S. K., & El-Kassar, A. N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213, 1264-1273.

Song, M., Fisher, R., & Kwoh, Y. (2019). Technological challenges of green innovation and sustainable resource management with large-scale data. Technological Forecasting and Social Change, 144, 361-368.

Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319-330.

Tseng, M. L., Islam, M. S., Karia, N., Fauzi, F. A., & Afrin, S. (2019). A literature review on green supply chain management: Trends and future challenges. Resources, Conservation and Recycling, 141, 145-162.

Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2017). An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 142, 1085-1097.

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